Improved prediction of protein ligand-binding sites using random forests

This article describes a novel method for predicting ligand-binding sites of proteins. this method uses only 8 structural properties as input vector to train 9 random forest classifiers which are combined to predict binding residues. these predicted binding residues are then clustered into some predicted ligand-binding sites. according to our measurement criterion,this method achieved a success rate of 0.914 in the bound state dataset and 0.800 in the unbound state dataset,which are better than three other methods: q-sitefinder,screen and morita's method. it indicates that the proposed method here is successful for predicting ligand-binding sites. © 2011 bentham science publishers.